modeling the effect of packet loss on speech quality: gp based symbolic regression

Wireless Access Research

Adil Raja June 2006

Modeling the Effect of Packet Loss on Speech Quality: GP Based Symbolic Regression


Adil Raja June 2006

Packet Loss Modeling Approaches

• Packet Based Approaches. Based on regression of packet loss parameters to MOS. Parameters include mean Loss rate, conditional loss probability

etc.

• Some approaches include: Markov Models {A. D. Clark} Regression Using Artificial Neural Networks.

{L. F. Sun et. al. and S. Mohammed et. Al}


Adil Raja June 2006

Speech Based Approaches

• Intrusive - ITU-T Recommendation P.862 (PESQ).

• Non-intrusive – ITU-T Recommendation P.563 (PSEAM).

• Non-intrusive PESQ – A. E. Conway.


Adil Raja June 2006

Previous Work

• ANN based Regression of network traffic metrics on speech quality.

• Useful Network loss Metrics. Mean Loss Rate. Means and Variances of Burst and Gap Length

Distributions. Codec Type and Packetization Interval. Inter Loss Distance/Gap Length.

• Packet loss was modeled using a Gilbert Model.• Results: - rtraining=0.9835; rvalidation=0.9821;

rtesting=0.9763


Adil Raja June 2006

The Gilbert Elliot Model

0

1

1

mmpn

i

i

p

1-q

No Loss State

1-p X=1 X=0q

Loss State

1

1

1

2

)1(1n

i

i

n

i

i imimq

Parameters of Geometrically distributed burst/gap lengths

Mean Burst length = 1/q

Variance of Burst Length Distribution = (1-q)/q2

Mean Gap Length = 1/p

Variance of Gap Length = (1-p)/p2

qp

p

1


Adil Raja June 2006

The Gilbert Model

• Packet loss can be simulated for certain values of p and q.

• During network operation bursts have to be captured for determining clp and ulp.

• The Gilbert model also models the packet loss due to jitter buffer discard/overflow.


Adil Raja June 2006

Current Approach

• Codecs – G.729 and G.723.1 and AMR-NB• Packet/frame loss simulation – Gilbert Model• Mean Loss rate ulp and clp were varied between 0-0.85 and 0-0.90

respectively.• Input Variables

mean loss rate, mean and variance of burst length distribution (VAD), mean and variance of gap length distribution (VAD), codec type and packetization interval.

VAD – Different packets have different importance {L. F. Sun | C. Hoene}.• Genetic Programming is used for Symbolic Regression.• Hyperbolic Tangent (Activation).• A total of 659 speech files out of which 35% were used for training, 15%

were used for validation and 50% were used for Speaker independent testing.

• Speech activity – 70-80%.


Adil Raja June 2006

Genetic Programming (GP)

• GP is a Machine Learning Technique inspired by biological evolution.


Adil Raja June 2006

Fitness: 0.0327 Test Fitness: 0.0437, r= 0.9748, r=0.9635 (tree5)


Adil Raja June 2006

Training (t5)


Adil Raja June 2006

Validation (t5)


Adil Raja June 2006

Fitness: 0.0342, Test Fitness: 0.0424, r=0.9737, r=0.9642


Adil Raja June 2006

Fitness=0.0463; Test Fitness=0.0528 r=0.9642, r=0.9566

F(X)=mysqrt(times(8,sin(X3)));

GP-MOS-LQO= -1.2442 * F(X) + 3.7511


Adil Raja June 2006

Training tn(28)


Adil Raja June 2006

Validation tn(28)


Adil Raja June 2006

Fitness=0.0466; Validation Fitness=0.0527; Testing Fitness=0.0401 r=0.9640, 0.9567, r=0.9696.

F(X)=minus(X3,times(5,kozasqrt(X3)));

GP-MOS-LQO= 0.8325*F(X) + 3.8482


Adil Raja June 2006

Training tn(25)


Adil Raja June 2006

Validation tn(25)


Adil Raja June 2006

Testing tn(25)

modeling the effect of packet loss on speech quality: gp based symbolic regression

Engineering